Bibliographic record
Abstract
To date, measurements of tunnel diode noise have dealt mainly with the negative conductance region, because the tunnel diode is an active circuit element only in this region. The noise has not been measured for reverse or near-forward biases due to the difficulties involving excessively low diode impedances in these regions. The purpose of this thesis is to show that, from the Esaki formulation for the direct-tunneling currents of a tunnel diode, in the bias regions where the electronic bands overlap, a simple theory can be developed relating the power spectrum associated with the direct-tunneling current noise to the direct current passing through the diode. This theory assumes that the two oppositely-flowing direct-tunneling currents in the Esaki junction are uncorrected and that both contribute full shot noise. The theory can be critically tested only in the bias regions where the noise is yet unstudied, and at sufficiently high frequencies that no contaminating 1/f noise exists. These conditions have been met experimentally and the noise measured quantitatively over the entire reverse and near-forward regions at a frequency of 4 Mc/s. Impedance-transforming networks and a very low-noise preamplifier suitable to the particular source strengths and impedances presented by the tunnel diode are developed for these measurements. A noise measurement technique is chosen from among several possible ones for the high degree of accuracy and smallest dependence on a good noise figure required for the tunnel diode source. The experimental results agree with the theory and vindicate the usual assumption that the two oppositely flowing direct-tunneling electron currents between two bands of a degenerately-doped semiconductor are uncorrelated. Noise measurements in the "valley" and far-forward region of the tunnel diode characteristic, where the diode current is not due to direct tunneling, do not agree with the simple two-current shot noise theory for direct-tunneling electron currents. Possible reasons for the enhanced noise measured in this region are advanced in the form of two models based on indirect-tunneling electrons via traps as the most important mechanism describing the excess or valley current. These models offer a possible explanation of the observed phenomena, but noise measurements alone appear insufficient to demonstrate unambiguously the detailed mechanisms producing either the excess current or the associated enhanced noise found throughout the valley and far-forward regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".